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As part of the CEE218X Fall Quarter 2020 roster, one of my assignments consisted in analyzing the education outcomes in the Bay Area. Here are the three different steps I achieved to carry out my study:
My conclusions are available in the Conclusions section.
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Before stating any conclusion, it is important to mention that the universe of the ACS datasets on educational attainment is “population 25 years or older”. Therefore, all the population 25 years or younger is not represented in these analysis. Part of it might be part - or is part when it comes to the Less than high school diploma - of the San Francisco county/Bay Area educational system. This is why the following assumptions may be proven wrong in the coming years depending on the evolution of the social diversity and inequities.
The San Francisco county shows a much more unequal educational attainment by race than the global Bay Area. The bar chart is much less centered and shows a linear variation instead; the White Alone race has a much higher educational attainment compared to the Some Other Race Alone and Black or African American categories while the Asian Alone category is less and less represented when the level of education increases. This same underrepresentation of the Some Other Race Alone and Black or African American populations among the highest educational attainment can be observed in the whole Bay Area, but the White Alone race representation is closest to its actual proportion of the population and the Asian Alone is a bit overrepresented, which is an indicator of social inclusion.
This in-depth analysis of the repartition of K-12 students with no internet access at home allowed me to consider reoccurring patterns on different scales:
Overall, the number of K-12 students with no internet access at home therefore appears to be a good indicator of social inequality among a population and a defined area; an internet access is essential to succeed in or even attend an academic curriculum, especially during the COVID-19 pandemic. While it was an incredible tool to get help or information, internet is now vital to be able to follow remote lessons, especially synchronous ones. Unfortunately, the highest percentages in the previous analyses occurred in the poorest areas of the Bay, which constitutes a vicious circle: children from poorest families don’t have all the tools required to properly follow their lessons, and therefore have lower chances to get a high educational attainment. It becomes then even more difficult for them to access higher incomes jobs.
Combining this indicator with the one studied in the first part (educational attainment by race), we could make some assumptions on the number of students with no internet at home by race, or even the repartition of different races among the areas of the county. For instance, it would be interesting to see if Some Other Race Alone and Black or African American categories have proportionally a higher rate of no internet access at home, if they live in higher proportions in the E SF, and to what extent it could explain their severe underrepresentation for the Bachelor’s degree or higher educational attainment.
This indicator is much limited by the scale of study. As mentioned for the Alameda county, studying the educational mobility at the scale of the county is not enough to understand the inequalities in the access to education. Also, as mentioned for the San Francisco county, other factors such as the global rent price of habitation are at stake when it comes to settle somewhere to pursue studies. Therefore, it is hard to come up with a solid conclusion with this indicator only. The same results in terms of Internal net and External net in two different counties might have very different interpretations and raise contrasted issues. However, it narrows down the real impact of the K-12 students with no internet access at home indicator; while having very good results with this indicator, the Solano county appeared not to be attractive for educational attainment categories usually aiming at pursuing higher level studies. This means that the proficiency and quality of educational institutions in the county is also a crucial factor in the choice of residential location to reach an higher educational attainment, a phenomenon I raised in my San Francisco county educational mobility analysis.
Also, it is hard to come up with any conclusion regarding a potential relation between educational mobility and race with these data only. Racial, social and access to education inequalities have proven to be much correlated with the perspective given by the two first indicators, but once again it is hard to come up with any tangible conclusion with the data on educational mobility. The study at a county scale is too general to give a proper insight on the reason of the arrivals/departures in a county, and neglects the differences in access to education among a county.